We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level features of the simulated detector events based on input observables of incident particles. This allows the dramatic increase of simulation speed. We demonstrate that this approach provides simulation precision which is consistent with the baseline and discuss possible implications of these results.
For the 12 GeV upgrade, the CLAS12 experiment has designed a Silicon Vertex Tracker (SVT) using single sided microstrip sensors fabricated by Hamamatsu. The sensors have graded angle design to minimize dead areas and a readout pitch of <span data-mathml="